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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
 
Gastroenterology. Author manuscript; available in PMC 2011 January 1.
Published in final edited form as:
PMCID: PMC2813393
NIHMSID: NIHMS148014

Predicting Clinical and Histological Outcomes Based on Standard Laboratory Tests In Advanced Chronic Hepatitis C

Marc G. Ghany, M.D.,1 Anna S. F. Lok, M.D.,2 James E. Everhart, M.D.,3 Gregory T. Everson, M.D.,4 William M. Lee, M.D.,5 Teresa M. Curto, M.P.H.,6 Elizabeth C. Wright, Ph.D.,7 Anne M. Stoddard, Sc.D.,6 Richard K. Sterling, M.D.,8 Adrian M. Di Bisceglie, M.D.,9 Herbert L. Bonkovsky, M.D.,10 Chihiro Morishima, M.D.,11 Timothy R. Morgan, M.D.,12 Jules L. Dienstag, M.D.,13 and and the HALT-C Trial Group

Abstract

Background & Aims

Predictors of clinical outcomes and histological progression among patients with chronic hepatitis C and advanced fibrosis are poorly defined. We developed statistical models to predict clinical and histological outcomes in such patients.

Methods

Baseline demographic, clinical, and histological data from Hepatitis C Antiviral Long-Term Treatment against Cirrhosis (HALT-C) Trial participants were subjected to multivariate analyses to determine their ability to predict clinical outcomes (ascites, spontaneous bacterial peritonitis, Child-Turcotte-Pugh score ≥7 on two consecutive visits, variceal bleeding, hepatic encephalopathy, and liver-related death) and histological outcome (≥2-point increase in Ishak fibrosis stage) during the 3.5 years of the trial.

Results

Of 1,050 randomized patients, 135 had one or more clinical outcomes a median of 23 (range 1–45) months after randomization. Factors associated with a clinical outcome in multivariate analyses were higher AST/ALT ratio, lower albumin, lower platelet count, higher total bilirubin, and more advanced Ishak fibrosis score (p<0.0001). The cumulative 3.5-year incidence of a clinical outcome was 2% in the lowest and 65% in the highest risk group. Of 547 patients without cirrhosis at baseline and at least one follow-up biopsy, 152 had a histological outcome. Independent variables associated with a histological outcome were higher body mass index, lower platelet count, and greater hepatic steatosis (p<0.0001).

Conclusion

In patients with chronic hepatitis C and advanced fibrosis, risk of clinical complications and fibrosis progression during 3.5 years can be predicted using baseline laboratory tests and histological data. Our models may be useful in counseling patients and determining the frequency of monitoring.

Keywords: Chronic hepatitis C, Clinical outcome, Histological outcome

INTRODUCTION

Chronic hepatitis C is an important cause of chronic liver disease, cirrhosis, and hepatocellular carcinoma (HCC)1 and the leading indication for liver transplantation in adults worldwide. Wise et al.2 reported an increase in premature death related to chronic hepatitis C during the decade spanning 1995–2004, and mathematical models predict that the number of deaths related to chronic hepatitis C will more than double in the next decade.3 Among patients with chronic hepatitis C, estimates suggest that cirrhosis will develop in 10% to 25% and HCC in 1% to 5% over a period of 20–30 years.4 A reliable estimate of the likelihood of disease progression is important for advising patients of their prognosis, including risk of hepatic decompensation, HCC, and death. Because surveillance for gastroesophageal varices and HCC are indicated in patients with advanced liver disease, and because cirrhotic patients merit more intensive monitoring to ensure timely referral for liver transplantation, clinicians require tests that can reliably predict clinical decompensation and histological progression of liver disease.

No tests are currently available to predict risk of decompensation in patients with chronic hepatitis C who have advanced fibrosis or cirrhosis. The Child-Turcotte-Pugh (CTP) score and the model for end-stage liver disease (MELD) are frequently used to assess this risk. However, the CTP score which was developed originally to predict operative risk of mortality associated with surgical portosystemic shunt surgery,5 was never validated for the purpose of predicting nonoperative survival or clinical outcome. Similarly, MELD which was designed to predict the survival of patients undergoing a transjugular intrahepatic portosystemic shunt, is used primarily to predict 90-day mortality of patients awaiting liver transplantation.6 The results of some studies suggest a poor correlation between MELD score and the development of late complications of cirrhosis, such as ascites and hepatic encephalopathy.7, 8

The Hepatitis C Antiviral Long-term Treatment against Cirrhosis (HALT-C) Trial was a randomized, controlled study to determine if long-term peginterferon therapy could reduce the risk of progression to cirrhosis, decompensated liver disease, and/or HCC in patients with chronic hepatitis C who had advanced fibrosis or cirrhosis.9 This large, prospectively followed cohort provided an ideal setting for the derivation of a model to predict clinical outcomes and progression to cirrhosis. The aims of this analysis were to identify baseline demographic, clinical, laboratory, virological, and histological factors associated with clinical outcomes and histological progression to cirrhosis and to develop models that predict clinical decompensation or progression of fibrosis based on demographic, histological and routinely available laboratory tests.

PATIENTS AND METHODS

The design of the HALT-C Trial has been described previously.9 Briefly, patients with previous nonresponse to standard interferon therapies who had advanced hepatic fibrosis on liver biopsy (Ishak fibrosis score ≥3)10 and no history of hepatic decompensation or HCC were treated with peginterferon alfa-2a (180 μg weekly) and ribavirin (1–1.2 g/day) (lead-in patients). Patients who had detectable serum HCV RNA at treatment week 20 met the definition of nonresponders and were randomized at week 24 to maintenance therapy (peginterferon alfa-2a 90 μg weekly) or no treatment for the next 3.5 years. Patients in whom serum HCV RNA was undetectable at week 20 met the definition of responders and continued combination therapy for a total of 48 weeks; however, “responder” patients in whom detectable HCV RNA appeared during treatment (breakthrough) or after stopping treatment (relapse) were offered enrollment into the randomized trial. “Express” patients were nonresponders to peginterferon/ribavirin who received therapy outside the HALT-C Trial, i.e., were not enrolled in the lead-in phase of the trial, and were enrolled directly into the randomized phase (Figure 1).

Figure 1
Risk Category and Time to Clinical Outcome. Figure derived from data in Table 3, Model a. The lowest risk group (lowest 50% of patients) was associated with a risk of decompensation of 2%, while the highest risk group (top 10% of patients) was associated ...

For lead-in patients, the baseline liver biopsy was performed at least 2 months following the last course of interferon therapy and within 12 months of enrollment. For express patients, a liver biopsy must have been performed within 18 months before randomization and at least 8 weeks after the end of the previous course of peginterferon and ribavirin. Liver biopsies were repeated 1.5 and 3.5 years after randomization. All biopsies were reviewed for inflammation, fibrosis, and other histological features, including steatosis, by a panel of hepatic pathologists. Inflammation was graded according to an 18-point histology activity index (HAI), and fibrosis was staged according to a 7-point scale as proposed by Ishak.10 Hepatic steatosis was graded based on the percentage of hepatocytes with fat as 0 (<1%), 1 (1%–5%), 2 (5%–33%), 3, (33%–67%), and 4 (>67%). The mean time from baseline laboratory tests to randomization was 162 days for lead-in patients, 516 days for breakthrough/relapse patients, and 7 days for express patients.

Assessment of clinical and histological outcomes

Patients were evaluated every 3 months during the 3.5 years of the randomized trial. Blood counts, liver blood test panel, and serum alpha fetoprotein (AFP) were tested at the local clinical center at each visit. Lead-in patients had an ultrasound examination of the liver 20 weeks and 12 months after enrollment and then every 12 months. Express patients had ultrasounds 6 months after enrollment and then every 12 months. Randomized subjects underwent esophagogastroduodenoscopy at the time of randomization and again at 1.5 years if they had varices or at 3.5 years if they did not have varices on the baseline endoscopy. For this analysis, treated and untreated patients from the randomized phase of the trial were combined, because the results of the trial showed that maintenance, low-dose peginterferon had no significant effect on clinical outcomes or histological progression.11

Pre-defined clinical outcomes included an increase in CTP score to ≥7 on two consecutive occasions 3 months apart, variceal bleeding, ascites, spontaneous bacterial peritonitis, hepatic encephalopathy, and/or death. For this analysis, HCC was excluded as a clinical outcome; our intent was to identify a model that would predict disease progression, but the pathogenesis of HCC was felt to be different from that of the other assessed clinical outcomes. Furthermore, because HCC can occur in the absence of cirrhosis or decompensated liver disease, the timing of its occurrence can be independent of disease progression.1214 Diagnostic criteria were established for each of the clinical outcomes, and an Outcomes Review Panel of three rotating investigators from the 10 HALT-C Trial clinical centers reviewed all outcome reports. Only clinical outcomes deemed to have met the predefined criteria were included in this analysis.

The trial was designed to enroll patients with Ishak fibrosis stage 3; however, in 72 patients, a biopsy taken near the time of enrollment was interpreted to show Ishak stage 2, despite the fact that they had had an earlier biopsy showing Ishak fibrosis stage ≥3. Histological outcome was defined as an increase in Ishak fibrosis score by ≥2 points on at least one of the follow-up biopsies 1.5 or 3.5 years after randomization (from stage 2 to 4–6, stage 3 to 5–6, or stage 4 to 6). Patients in whom a biopsy pair was not available were excluded from this analysis (n=75). The mean biopsy length was 1.8 cm (median 1.6, range 0.4 to 3.4 cm), and 13% were fragmented.

Statistical Analyses

We used multivariable time-to-event analytic methods to explore factors associated with time to the first clinical outcome and time to histological outcome as indicated by a ≥2-point increase in Ishak fibrosis score, which was coded as a continuous variable (2, 3, 4, 5, 6). First, individual factors were explored in relation to each outcome. For multivariable models, factors that were associated with the outcome on bivariate analysis with a p value <0.1 and factors previously reported to be associated with outcomes (age, race, gender and BMI) were included in the initial multivariable analysis, as was treatment assignment. Variables that were no longer statistically significant in the multivariate analyses were deleted to create the final models.

For time to first clinical outcome, we used Cox proportional hazards regression analysis. Data were censored at the patient’s last follow-up visit or at 1400 days (3.8 years) after randomization, whichever occurred first. The 3.8 rather than 3.5 year mark provided a window period for performance of the 3.5 year liver biopsy and for confirming a CTP score ≥7 that first occurred at the 3.5 year visit. We used the resulting individual estimates of the final model to divide the sample into four risk groups. Cumulative incidence rates of clinical outcomes among these groups were determined by Kaplan Meier analysis. For time to histological progression, we used the complementary log-log regression analysis since the time-to-event analysis was grouped (1.5 and 3.5 years of follow-up). All analyses were performed at the Data Coordinating Center (New England Research Institutes, Watertown, MA) with SAS statistical software release 9.1 (SAS Institute, Cary, NC).

RESULTS

Clinical Outcome

A total of 1,050 patients with chronic hepatitis C and advanced fibrosis or cirrhosis were followed for predefined clinical outcomes reflecting the presence of decompensated liver disease over the 3.5 years of the randomized phase of the HALT-C Trial, and, in 135 (13%), a clinical outcome developed. An increase in CTP score to ≥7 on two successive study visits was the most frequently observed initial outcome, accounting for two-thirds of the clinical outcomes (Table 1). In 48% of patients with a CTP score ≥7 as their first outcome, at least one event representing hepatic decompensation (death, ascites, spontaneous bacterial peritonitis, variceal hemorrhage, or hepatic encephalopathy) developed subsequently a median of 8.2 (range 0.6–37.7) months after first achieving an increase in CTP score to >7. The most frequent initial decompensation event was the development of ascites, accounting for 16% of outcomes.

Table 1
Distribution of First Clinical Outcome1

Baseline demographic, laboratory, and liver histology data and univariate hazard ratios for the 135 patients who had a clinical outcome and the 915 patients who did not are shown in Table 2. On univariate analysis clinical and laboratory measures associated with subsequent hepatic decompensation (p<0.05) included higher body mass index (BMI), lower cigarette consumption, treatment stratification (being a lead-in and express patients versus breakthrough/relapser) and the following laboratory results: increased aspartate aminotransferase (AST), alanine aminotransferase (ALT), AST/ALT ratio, total bilirubin, International Normalized Ratio (INR) of prothrombin time, AFP, iron saturation, and ferritin; and lower HCV RNA, albumin, platelets, creatinine, and white blood cell count (WBC). For histological and endsocopic variables, periportal inflammation, composite histological activity index (HAI) scores, Ishak fibrosis scores, and the presence of esophageal varices were associated with decompensation. Treatment assignment was not associated with a clinical outcome.

Table 2
Baseline Characteristics of Patients by Clinical Outcome (n=1050)

To determine which variables were independent predictors of clinical outcomes, we first included baseline laboratory variables from the univariate analysis, omitting histological or endoscopic variables. Higher AST/ALT ratio, higher total bilirubin concentration, lower albumin concentration, and lower platelet count were associated with increased risk in a multivariate Cox proportional hazards model (Table 3, Model a). With the addition of histological and endoscopic information, we found that only Ishak fibrosis score was associated with risk of clinical outcome. The inclusion of the Ishak fibrosis score led to a modest attenuation of the log10 AST/ALT ratio HR (from 3.34 to 3.06) and had no effect on the association with the other laboratory measures (Table 3, Model b).

Table 3
Cox proportional hazards survival analysis of factors associated with time to first clinical outcome (n=1050): Hazard rate ratio, 95% confidence interval and p-value.

Breakthrough/relapser patients may have benefited from a longer duration of combination therapy prior to randomization (48 weeks compared to 24 weeks for lead-in and express patients). To address this issue we performed a second analysis controlling for enrollment status (breakthrough/relapser versus lead-in and express patients). Breakthrough/relapser was significant in model a (HR 0.41, p-value 0.02) but not model b. All the other previously identified variables remained significant in both models with minor changes in the HR (data not shown). If breakthrough/relapsers were excluded from the analysis, the previously identified variables in model a remained unchanged but the HR for log AST/ALT decreased from 3.34 to 3.0 and for total bilirubin increased from 1.82 to 2.13; the HRs for the other variables remained the same (Table 3). Interestingly, in model b, Ishak fibrosis was just below statistical significance (p=.06, HR decreased from 1.31 to 1.18) when breakthrough/relapsers were excluded.

We found a significant interaction between treatment (treatment group versus control group) and total bilirubin on the association with clinical outcome in both models a and b. Total bilirubin had a stronger effect in the control group compared to the treatment group with a hazard ratio of 3.12 in the control group and 1.29 in the treatment group (model a) and 3.05 in the control group and 1.41 in the treatment group (model b). Separating the groups into either fibrosis/cirrhosis strata or including patients with HCC as the first primary outcome (n=27) resulted in only minor differences of the variables associated with clinical decompensation in univariate analysis (data not shown). Inclusion of patients whose first primary outcome was HCC had a minimal effect on hazard ratios in the multivariate analysis (log AST/ALT HR decreased from 3.34 to 2.36, total bilirubin decreased from 1.82 to 1.79, while HR for albumin 0.20 to 0.21 and platelets 0.59 to 0.59 were unchanged.

The model to predict clinical outcome using only baseline laboratory variables (Model a) is represented by Ra = 1.21*log10 (AST/ALT) + 0.60* total bilirubin (mg/dl) −1.60*albumin (g/dl) −0.52*platelets (per mm3)/50, where R denotes the linear predictor portion of the equation. Ranges of R within a risk group for this model and for Model b (including the Ishak fibrosis score) are provided in Table 4. The lowest risk group (lowest 50% of patients) was associated with a risk of decompensation of 2%, while the highest risk group (top 10% of patients) was associated with a risk of hepatic decompensation of 65% over 3.5 years (Table 4, Model a, Figure 2). An example of the use of the model is provided as a footnote to Table 4.

Table 4
Risk Stratification by Platelets, Albumin, AST/ALT, total bilirubin and Cox Regression Hazards Models to Predict Clinical Outcome.

Histological Outcome (≥2-point increase in Ishak fibrosis score)

A second analysis was performed to determine clinical predictors of a ≥2-point increase in Ishak fibrosis score among patients without cirrhosis (Ishak fibrosis score <5) at baseline. For this analysis, 72 patients with Ishak stage 2, 315 with Ishak stage 3, and 160 with Ishak stage 4 at baseline who had at least one of the two subsequent protocol biopsies were included. Of these 547 patients, 529 underwent liver biopsy at 1.5 years, 460 underwent liver biopsy at 3.5 years and 443 had both follow-up biopsies. One hundred and fifty-two (28%) patients experienced a ≥2-point increase in Ishak fibrosis score. Baseline data in the 152 patients who had a ≥2-point increase in Ishak fibrosis score and those who did not are shown in Table 5. Features associated with a histological outcome on univariate analysis were higher BMI; higher ALT, AST, AST/ALT ratio, alkaline phosphatase, total bilirubin, INR, iron saturation, ferritin, and AFP; as well as lower albumin, platelet count, and WBC count. In addition, less portal inflammation and greater steatosis on baseline liver biopsy and the presence of esophageal varices were associated in the univariate analysis with ≥2-point increases in fibrosis score. On multivariate analysis, high BMI and low platelet count were the only noninvasive measures that were associated with a ≥2-point increase in Ishak fibrosis score (Table 6). No evidence of interaction between these variables and treatment assignment was observed. With the addition of findings from invasive testing, only hepatic steatosis was associated with a ≥2-point increase in Ishak fibrosis score (Table 6). Inclusion of hepatic steatosis attenuated the effect of BMI in the multivariate model (data not shown).

Table 5
Univariate Analysis of Baseline Characteristics with time to Two-Point Increase (TPI) in Ishak Fibrosis Score, Controlling for Treatment Assignment (n=547)
Table 6
Complementary Log-Log Regression Analysis Of Factors Associated With Time To Two-Point Increase in Ishak Fibrosis Score (Fibrosis Patients Only n=547)

Platelet count had a nonlinear relationship with a ≥2-point increase in Ishak fibrosis score, such that including a quadratic term for platelets (platelet count squared) provided a better indication of the effect of platelet count than platelet count itself. This relationship is demonstrated in Table 7, in which we found a strong association of platelet count with subsequent ≥2-point increase in Ishak fibrosis score for platelet counts less than 150,000/mm3, but no association for platelet count >150,000/mm3. The final model to predict the risk of a ≥2-point increase in Ishak fibrosis score is shown in Table 6. Patients with a low platelet count may have been understaged on the initial liver biopsy. To address the issue of sampling error, we excluded patients with a baseline platelet count <100 K/mm3 (n=35) from the analysis. This had no effect on the variables identified in the univariate analysis and in the multivariate model to predict a two-point increase in Ishak fibrosis score. The HR remained almost unchanged for all the variables (BMI 1.03 to 1.03, platelets/50K/mm3 0.26 to 0.23, hepatic steatosis 1.37 to 1.35 and platelets/50K/mm3 squared 1.12 to 1.14)

Table 7
Risk Stratification by Platelet Count to Predict Two-point increase (TPI) in Ishak fibrosis score

DISCUSSION

Clinical outcomes developed in 13% and histological outcomes in 28% of subjects with advanced chronic hepatitis C followed in the HALT-C Trial for 3.5 years. Based upon baseline data obtained from these patients, we developed separate models to predict clinical and histological outcomes. The best model to predict clinical outcomes consisted of 4 variables: log10 AST/ALT ratio, platelet count, total bilirubin, and albumin. The model to predict histological outcome included baseline BMI, platelet count, and hepatic steatosis.

In several studies, prognostic factors for hepatic decompensation (excluding HCC) have been examined in patients with HCV-related cirrhosis. Independent factors associated with decompensation in these studies included older age at infection, higher baseline bilirubin level, lower albumin level, lower platelet count, stigmata of chronic liver disease on physical examination, the presence of esophageal varices and absence of interferon therapy.1520 Differences in predictive factors identified in our prospective study (higher baseline log10 AST/ALT ratio, total bilirubin, lower platelet count, and lower albumin) compared to the other studies may have resulted from use of different definitions of hepatic decompensation, from the limitations of mostly retrospective studies, and from differences in patient populations between other studies and ours. For example, the HALT-C Trial population included participants with advanced fibrosis or cirrhosis but no patients with earlier histological stages, while some of the previous studies did include patients with less advanced histological disease.

Interestingly, despite dissimilarities among previous studies and this one, baseline serum bilirubin, albumin, and platelet count were identified consistently as independent factors associated with hepatic decompensation or the development of HCC. These variables are recognized surrogate markers of disease severity, reflecting either derangements in hepatic synthetic (albumin) and excretory (bilirubin) function or severity of portal hypertension (platelet count). Although previously shown to be useful in the diagnosis of cirrhosis,21, 22 AST/ALT ratio has not been reported previously to be predictive of clinical decompensation. Disease modifying variables, e.g., age and alcohol use were not identified in the model, probably because they were outweighed by variables that were associated with disease severity, the age range of the HALT-C cohort was narrow, and few individuals continued to drink alcohol after enrollment into the study. The addition of endoscopic data (i.e., presence of esophageal varices) and of liver biopsy data did not improve significantly the accuracy of the model to predict a clinical outcome. Failure of liver fibrosis to predict clinical outcome may reflect sampling error associated with liver biopsy, suggesting that laboratory tests may be more reliable than histology for predicting clinical outcomes.

This clinical model could prove useful in determining how to allocate resources for patient monitoring. Because the prognosis of patients with HCV-related compensated cirrhosis is so variable—excellent in some studies (80% 10-year survival15) and poor in others such as ours, (in which decompensation occurred in approximately a third of patients followed prospectively for 3.5 years11) potentially those at highest risk of decompensation could be monitored more intensively, while those with lower risk scores could undergo less frequent monitoring.

Previous cross-sectional liver-biopsy studies (in which a single liver biopsy is correlated with laboratory features) have identified male gender, alcohol consumption >50g per day, and age >40 years as significant correlates of advanced fibrosis,23, 24 while paired liver-biopsy studies (in which histology is assessed at two points in time) have identified older age, higher serum ALT, more severe necroinflammatory activity, and higher baseline fibrosis stage as predictors of progression of fibrosis. None of these variables, however, predicted histological outcome in the present analysis.25, 26 Instead, higher baseline BMI, lower platelet count, and greater hepatic steatosis were independent predictors of a >2-point increase in liver fibrosis during the observation period in this trial. The difference between our findings and those in other studies may be related to how histological outcome was assessed in the HALT-C Trial (≥2 increase in Ishak fibrosis stage) compared to the approach in other studies (1-point increase in fibrosis stage). Furthermore, other studies included patients with a spectrum of fibrosis stages, as opposed to the HALT-C Trial, which was confined to patients with more advanced liver disease. Finally, not all previous studies examined BMI or steatosis.

Obesity has been associated with more rapid fibrosis progression. Obesity may promote fibrogenesis via proinflammatory cytokines,27 insulin resistance, or hepatic steatosis.2832 In a separate analysis of weight related features (including glucose and insulin levels, BMI, and histological features of fatty liver) among the HALT-C Trial cohort, we showed that hepatic steatosis was associated with an increased rate of outcomes among patients with bridging fibrosis but not cirrhosis.33 This analysis extends those findings by demonstrating that hepatic steatosis was a predictor of fibrosis progression independent of laboratory markers for liver disease severity. Together, the results of the HALT-C Trial add to the growing evidence that obesity and hepatic steatosis are linked to disease progression.31, 3438 These findings raise the question of whether weight loss might slow fibrosis progression, which merits confirmation in prospective studies.

Low baseline platelet count was the only predictive variable present in both clinical and histological progression models, probably because thrombocytopenia is such a dominant indicator of advanced disease, reflecting both portal hypertension (splenic sequestration) and decreased hepatic synthetic function (thrombopoeitin production). The relationship between platelet count and fibrosis progression was not linear in the complementary log-log model; instead, our data showed that the rate of histological outcomes remained constant as the platelet count decreased from the normal range to 150 K/mm3 but increased sharply as the platelet count fell below this value (Table 7). We cannot discount the possibility, however, that the rapidity of fibrosis progression observed in patients starting with a low platelet count was an artifact, confounded by the presence of cirrhosis which was not detected on the baseline liver biopsy. Exclusion of these patients from the analysis resulted in a minimal change in the point estimates of the model to predict a two-point increase in fibrosis.

The advantages of the model we derived for predicting clinical progression are its derivation in a prospective, rather than retrospective, trial and its reliance on standard laboratory tests. If our model can be confirmed in other populations, a simple web-based calculator could be made available, as is the case for calculating MELD scores, which should allow clinicians to make a rapid assessment of the risk for hepatic decompensation, enhancing their ability to counsel patients about prognosis and to plan an appropriate monitoring schedule.

Our study has several limitations. Because half of our patients received low-dose peginterferon alfa-2a for 3.5 years, potentially, therapy could have confounded our analyses; however, because maintenance therapy was shown to have no effect on clinical or histological outcomes in the trial,11 and treatment was shown in a post hoc exploratory analysis not to have any significant effect on the identified baseline variables including serum ALT, total bilirubin, albumin and platelet count,11 we felt justified in combining patients receiving maintenance peginterferon and untreated control patients in the analysis. We also performed several analyses to control for any potential effect of treatment on outcomes. Breakthrough/relapsers were the subgroup who could have benefited from longer treatment. However, controlling for or excluding breakthrough/relapser had minor effects on both the clinical and histological models. Also, potentially influencing the timing of complications was the variability of intervals between the baseline laboratory values and the baseline biopsy and between baseline laboratory values and the time of randomization. A small percentage of patients withdrew prior to completion of the study. Because we do not know the outcome of these patients, we cannot assess how this may have influenced the final model. Finally, whether these models might be applied to patients with milder degrees of liver disease remains to be established.

In conclusion, we have developed models based on standard laboratory tests and histological assessment to predict clinical or histological progression in patients with chronic hepatitis C and advanced liver disease. The clinical model in particular can be used to estimate the risk of future hepatic decompensation, allowing more intense monitoring and early detection of hepatic complications in those at highest risk for these outcomes. Our models should be validated in other cohorts and, if confirmed, may aid in assessing prognosis in patients with chronic hepatitis C and advanced hepatic fibrosis.

Supplementary Material

01

Acknowledgments

This study was supported by the National Institute of Diabetes & Digestive & Kidney Diseases (contract numbers are listed below). Additional support was provided by the National Institute of Allergy and Infectious Diseases (NIAID), the National Cancer Institute, the National Center for Minority Health and Health Disparities and by General Clinical Research Center and Clinical and Translational Science Center grants from the National Center for Research Resources, National Institutes of Health (grant numbers are listed below). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Center for Research Resources or the National Institutes of Health. Additional funding to conduct this study was supplied by Hoffmann-La Roche, Inc., through a Cooperative Research and Development Agreement (CRADA) with the National Institutes of Health.

In addition to the authors of this manuscript, the following individuals were instrumental in the planning, conduct and/or care of patients enrolled in this study at each of the participating institutions as follows: University of Massachusetts Medical Center, Worcester, MA: (Contract N01-DK-9-2326) Gyongyi Szabo, MD, Barbara F. Banner, MD, Maureen Cormier, RN, Donna Giansiracusa, RN

University of Connecticut Health Center, Farmington, CT: (Grant M01RR-06192) Gloria Borders, RN, Michelle Kelley, RN, ANP

Saint Louis University School of Medicine, St Louis, MO: (Contract N01-DK-9-2324) Bruce Bacon, MD, Brent Neuschwander-Tetri, MD, Debra King, RN

Massachusetts General Hospital, Boston, MA: (Contract N01-DK-9-2319, Grant M01RR-01066; Grant 1 UL1 RR025758-01, Harvard Clinical and Translational Science Center) Raymond T. Chung, MD, Andrea E. Reid, MD, Atul K. Bhan, MD, Wallis A. Molchen, Cara C. Gooch

University of Colorado School of Medicine, Denver, CO: (Contract N01-DK-9-2327, Grant M01RR-00051, Grant 1 UL1 RR 025780-01) S. Russell Nash, MD, Jennifer DeSanto, RN, Carol McKinley, RN

University of California - Irvine, Irvine, CA: (Contract N01-DK-9-2320, Grant M01RR-00827) John C. Hoefs, MD, John R. Craig, MD, M. Mazen Jamal, MD, MPH, Muhammad Sheikh, MD, Choon Park, RN

University of Texas Southwestern Medical Center, Dallas, TX: (Contract N01-DK-9-2321, Grant M01RR-00633, Grant 1 UL1 RR024982-01, North and Central Texas Clinical and Translational Science Initiative) Thomas E. Rogers, MD, Janel Shelton, Nicole Crowder, LVN, Rivka Elbein, RN, BSN, Nancy Liston, MPH

University of Southern California, Los Angeles, CA: (Contract N01-DK-9-2325, Grant M01RR-00043) Karen L. Lindsay, MD, MMM, Sugantha Govindarajan, MD, Carol B. Jones, RN, Susan L. Milstein, RN

University of Michigan Medical Center, Ann Arbor, MI: (Contract N01-DK-9-2323, Grant M01RR-00042, Grant 1 UL1 RR024986, Michigan Center for Clinical and Health Research) Robert J. Fontana, MD, Joel K. Greenson, MD, Pamela A. Richtmyer, LPN, CCRC, R. Tess Bonham, BS

Virginia Commonwealth University Health System, Richmond, VA: (Contract N01-DK-9-2322, Grant M01RR-00065) Mitchell L. Shiffman, MD, Melissa J. Contos, MD, A. Scott Mills, MD, Charlotte Hofmann, RN, Paula Smith, RN

Liver Diseases Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD: T. Jake Liang, MD, David Kleiner, MD, PhD, Yoon Park, RN, Elenita Rivera, RN, Vanessa Haynes-Williams, RN

National Institute of Diabetes and Digestive and Kidney Diseases, Division of Digestive Diseases and Nutrition, Bethesda, MD: Leonard B. Seeff, MD, Patricia R. Robuck, PhD, Jay H. Hoofnagle, MD

University of Washington, Seattle, WA: (Contract N01-DK-9-2318) David R. Gretch, MD, PhD, Minjun Chung Apodaca, BS, ASCP, Rohit Shankar, BC, ASCP, Natalia Antonov, M. Ed.

New England Research Institutes, Watertown, MA: (Contract N01-DK-9-2328) Kristin K. Snow, MSc, ScD, Linda J. Massey, Deepa Naishadham, MA, MS

Armed Forces Institute of Pathology, Washington, DC: Zachary D. Goodman, MD

Data and Safety Monitoring Board Members: (Chair) Gary L. Davis, MD, Guadalupe Garcia-Tsao, MD, Michael Kutner, PhD, Stanley M. Lemon, MD, Robert P. Perrillo, MD

Abbreviations

HALT-C
hepatitis C antiviral long-term treatment against cirrhosis
AST
aspartate aminotransferase
ALT
alanine aminotransferase
CTP
Child-Turcotte-Pugh
MELD
model for end-stage liver disease
HCC
hepatocellular carcinoma
TPI
two-point increase
HCV RNA
hepatitis C virus ribonucleic acid
AFP
alfa fetoprotein
BMI
body mass index
INR
International Normalized Ratio
WBC
white blood cell
HAI
histological activity index
Author Degrees, Participation and Emails
Marc G. Ghany1, M.D.,1,2,3,4,5,MarcG/at/bdg10.niddk.nih.gov
Anna S. F. Lok2, M.D.,1,2,3,4,5,7aslok/at/umich.edu
James E. Everhart3, M.D.,1,3,4,5je17g/at/nih.gov
Gregory T. Everson4, M.D.,1,2,3,4,5,7Greg.Everson/at/uchsc.edu
William M. Lee5, M.D.,1,2,3,4,5,7william.lee/at/utsouthwestern.edu
Teresa M. Curto6, M.P.H.,3,6tcurto/at/neriscience.com
Elizabeth C. Wright7, Ph.D.,3,6wrightel/at/niddk.nih.gov
Anne M. Stoddard6, Sc.D.,3,6astoddard/at/neriscience.com
Richard K. Sterling8, M.D.,1,2,5,7RKSterli/at/hsc.vcu.edu
Adrian M. Di Bisceglie9, M.D.,1,2,5,7dibiscam/at/slu.edu
Herbert L. Bonkovsky10, M.D.,1,2,5,7Herbert.bonkovsky/at/carolinashealthcare.org
Chihiro Morishima11, M.D.,1,2,5,7chihiro/at/u.washington.edu
Timothy R. Morgan12, M.D.,1,2,5,7timothy.morgan/at/va.gov
Jules L. Dienstag13, M.D.,1,2,5,7jdienstag/at/partners.org
1=study concept and design
2=acquisition of data
3=analysis and interpretation of data
4=drafting of manuscript
5=critical revision of manuscript
6=statistical analyses
7=obtained funding

Section B

In addition, many of the HALT-C Trial investigators have other associations with industry relating to the area of hepatitis C, and, to achieve the highest level of disclosure, we list these for you as well.

A. S. F. Lok: Schering-Plough Corporation – Consultant and receives research support. Vertex Pharmaceuticals – consultant. Idenix Pharmaceuticals – receives research support. Eisai Pharmaceuticals – receives research support.

G. T. Everson receives research support from Schering-Plough, Pharmasset, GlobeImmune, Source, Novartis/Human Genome Sciences, and GlaxoSmithKline; and is a consultant and receives research support from Vertex Pharmaceuticals.

W. M. Lee receives research support from Aegerion, GlobeImmune, Orasure, Schering-Plough, Siemens Diagnostics, and Vertex Pharmaceuticals.

A.M. Stoddard: Equity interest in Johnson & Johnson, Procter & Gamble, Bristol-Myers Squibb, and Elan.

R. K. Sterling is a consultant and on the speaker’s bureau for Schering-Plough; is on an advisory board for Vertex Pharmaceuticals; and is a consultant and receives research support from Wako Chemicals USA.

A. M. Di Bisceglie is a consultant, on the speaker’s bureau and receives research support from Idenix Pharmaceuticals; is a consultant for Schering-Plough, Novartis, Bristol-Myers-Squibb and Abbott; is a consultant and receives research support from Vertex Pharmaceuticals, Anadys, GlobeImmune, Pharmasset, Sci-Clone, Gilead Sciences and Novartis.

H. L. Bonkovsky: Glaxo Smith Kline—serves on a data monitoring and safety committee. Boehringer-Ingelheim and Infacare Pharmaceuticals—served as a paid consultant. Merck—receives research support. Novartis Pharmaceuticals – consultant and receives research support. Ovation, Inc—consultant and on speakers’ bureau. Schering-Plough Corporation – receives research support. Vertex Pharmaceuticals – receives research support.

T. R. Morgan is on the speaker’s bureau and receives research support from Schering-Plough; and is a consultant and receives research support from Vertex Pharmaceuticals.

J. L. Dienstag: Vertex Pharmaceuticals – Receives research support. Serves on a Data Monitoring Committee for Schering-Plough Research Institute and Human Genome Sciences. Boehringer-Ingelheim – ad hoc hepatitis advisory board.

Footnotes

Financial Disclosures

Financial relationships of the authors with Hoffmann-La Roche, Inc., are as follows: A. S. F. Lok is a consultant; G. T. Everson is a consultant, on the speaker’s bureau, and receives research support; W. M. Lee receives research support; R. K. Sterling is consultant, on the speaker’s bureau and receives research support; A.M. Di Bisceglie is a consultant, on the speaker’s bureau, and receives research support; H. L. Bonkovsky receives research support; and T. R. Morgan is consultant, on the speaker’s bureau and receives research support;

Authors with no financial relationships related to this project are: M. G. Ghany, J. E. Everhart, T. M. Curto, E. C. Wright, A. M. Stoddard, C. Morishima, and J. L. Dienstag.

This is publication #40 from the HALT-C Trial Group.

*The HALT-C Trial was registered with clinicaltrials.gov (#NCT00006164).

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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